In communication networks, there is always a challenge to obtain good performance and capacity for a given communications protocol, its parameters and the physical environment in which the communication network is deployed.
Existing real-time audio-visual communication services, including the 3GPP (3rd Generation Partnership Project) technical standard TS 26.114 (MTSI; Multimedia Telephony Service for Internet Protocol Multimedia Subsystem) and the profile called IR.94 in GSMA (the Global System for Mobile Communications Association), use SIP/SDP (Session Initiation Protocol/Session Description Protocol) as signaling to configure and setup a call between two user equipment (UE), and use the Internet Protocol (IP), the User Datagram Protocol (UDP) and the Real-time Transport Protocol (RTP) to encapsulate a (compressed representation of) media data during transport, on top of any chosen physical transport medium.
One approach to handle service and mobile broadband (MBB) transport is to configure an application run on a connected device to use a constant bitrate and to configure and dimension the transport resources to, with a sufficiently high probability, provide the needed resource.
Another approach is to configure the application to adapt to changing transport prerequisites in the media end point devices (i.e., at connected devices). This adaptation could be based on measurements in the end points and service specific implementations that perform the adaptation. The adaptation may include stopping the transmission of media components, such as the video component. For instance, for the GSMA Voice over Long Term Evolutional (VoLTE), Open Mobile Alliance (OMA) Device Management mechanism is intended to be used to provision a start bit rate to be used by the video coder application of a connected device.
In general terms, the user experience of services involving real time media, such as speech and conversational video, in a communications environment with constrained transport resources, such as in a public mobile broadband cell, is negatively impacted by packet loss, jitter and possibly packet reordering. Developing and configuring the service to adapt to varying transport prerequisites may be challenging, especially when channel conditions are subject to rapid, sporadic changes.
Providing the network developer and network service provider with tools for managing this uncertainty by exposing explicit information is one approach for providing a better user experience. Approaches based on having a constant bit rate may not be able to utilize the capacity available in a heterogeneous transport network; the maximum bitrate used is set according the lowest expected capability.
Further, transport conditions in a mobile broadband cell may be changing so fast that an end point device cannot detect changes as they occur. Also, the change itself may not be easily predictable. The transmission capacity of the wireless link for a particular user and radio bearer varies. The actual maximum transmission capacity may only be determined by having a standing queue for the radio scheduler scheduling packets of the user and radio bearer. In general terms, a standing queue is equivalent to delay, which is undesirable for a real-time application.
Further, IP protocols do not give any indication regarding how under-utilized the currently available capacity for a flow is. The amount of network traffic must be quickly increased to successfully probe after available capacity when no signs of over-utilization are present. When this rapid increase of network traffic reaches the actual capacity limit of the network a significant overshot may occur due to the delay in the network. In such cases a significant queue, and thus one-way delay, will be built up in the network, thus reducing the user experience of the service. At least these reasons may make it difficult to deploy probing, thus preventing the end point device to adapt in pace with the changing network conditions.
Current End-to-End algorithms, even if performing predictions, such as the Sprout transport protocol may only observe the network traffic flows to and from individual UEs and are not capable of registering or predicting the impact from other UEs, including mobility events in and out from each cell.
Another issue is that when the application is temporarily limited in the amount of data it wants to send, the network is, below its available capacity limit, not enough excited to produce any signals from which current and future behavior can be predicted from.
In conclusion, the above disclosed methods to estimate available network capacity makes it hard to accurately match actually available network capacity to the needed capacity at the media sender, leading to bad QoE, such as for example one or more of lower-than-necessary media quality due to under-utilization of available capacity resources, loss of media data due to over-use of capacity resources, delay and delay variation due to (long or short-term) over-use of capacity resources.
Hence, there is still a need for providing improved network information in a communications network.